Neural network ansatz for periodic wave functions and the homogeneous electron gas

We design a neural network Ansatz for variationally finding the ground-state wave function of the homogeneous electron gas, a fundamental model in the physics of extended systems of interacting fermions. We study the spin-polarized and paramagnetic phases with 7, 14, and 19 electrons over a broad range of densities from r_s=1 to r_s=100, obtaining similar or higher accuracy compared to a state-of-the-art iterative backflow baseline even in the challenging regime of very strong correlation. Our work extends previous applications of neural network Ansätze to molecular systems with methods for handling periodic boundary conditions, and makes two notable changes to improve performance: splitting the pairwise streams by spin alignment and generating backflow coordinates for the orbitals from the network. We illustrate the advantage of our high-quality wave functions in computing the reduced single-particle density matrix. This contribution establishes neural network models as flexible and high-precision Ansätze for periodic electronic systems, an important step towards applications to crystalline solids.